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Modeling of the ARMA random effects covariance matrix in logistic random effects models

Title
Modeling of the ARMA random effects covariance matrix in logistic random effects models
Authors
Lee, KeunbaikJung, HoiminYoo, Jae Keun
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2019
Journal Title
STATISTICAL METHODS AND APPLICATIONS
ISSN
1618-2510JCR Link

1613-981XJCR Link
Citation
STATISTICAL METHODS AND APPLICATIONS vol. 28, no. 2, pp. 281 - 299
Keywords
Cholesky decompositionLongitudinal dataHeteroscedasticRepeated outcomes
Publisher
SPRINGER HEIDELBERG
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Logistic random effects models (LREMs) have been frequently used to analyze longitudinal binary data. When a random effects covariance matrix is used to make proper inferences on covariate effects, the random effects in the models account for both within-subject association and between-subject variation, but the covariance matix is difficult to estimate because it is high-dimensional and should be positive definite. To overcome these limitations, two Cholesky decomposition approaches were proposed for precision matrix and covariance matrix: modified Cholesky decomposition and moving average Cholesky decomposition, respectively. However, the two approaches may not work when there are non-trivial and complicated correlations of repeated outcomes. In this paper, we combined the two decomposition approaches to model the random effects covariance matrix in the LREMs, thereby capturing a wider class of sophisticated dependence structures while achieving parsimony in parametrization. We then used our proposed model to analyze lung cancer data.
DOI
10.1007/s10260-018-00440-y
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자연과학대학 > 통계학전공 > Journal papers
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